A multi-energy load prediction of a building using the multi-layer perceptron neural network method with different optimization algorithms
Since cooling and heating loads are recognized as key characteristics for evaluating the energy efficiency of buildings, it appears indisputable that they must be predicted and analyzed for residential structures. Accordingly, the multi-layer perceptron neural network is applied for predicting the h...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2023-01-01
|
| Series: | Energy Exploration & Exploitation |
| Online Access: | https://doi.org/10.1177/01445987221112250 |
| _version_ | 1811316397023690752 |
|---|---|
| author | Zhongzhen Yan Xinyuan Zhu Xianglong Wang Zhiwei Ye Feng Guo Lei Xie Guiju Zhang |
| author_facet | Zhongzhen Yan Xinyuan Zhu Xianglong Wang Zhiwei Ye Feng Guo Lei Xie Guiju Zhang |
| author_sort | Zhongzhen Yan |
| collection | DOAJ |
| description | Since cooling and heating loads are recognized as key characteristics for evaluating the energy efficiency of buildings, it appears indisputable that they must be predicted and analyzed for residential structures. Accordingly, the multi-layer perceptron neural network is applied for predicting the heating and cooling loads using the experimental dataset. The used dataset is obtained by monitoring the impact of the building's dimensions on energy consumption. To optimize the training process of the multi-layer perceptron neural network, several optimizers are employed. Besides, different statistical performance indicators are considered to see which selected optimizer outperforms the rest in terms of accuracy and authenticity. The obtained results emphasize the remarkable performance of adaptive chaotic grey wolf optimization, which can be used to train the multi-layer perceptron neural network and forecast the buildings’ energy consumption with the highest accuracy. According to the obtained results, the hybrid multi-layer perceptron neural network- adaptive chaotic grey wolf optimization method demonstrates the best performance. The optimum number of neurons in the hidden layer is obtained to be 15. Also, based on the statistical performance indicators, the selected method reveals an R 2 of 0.9123 and 0.9419 for cooling and heating loads, respectively. |
| first_indexed | 2024-04-13T11:49:21Z |
| format | Article |
| id | doaj.art-4542cc601a164d65b55f96ce3653c6a9 |
| institution | Directory Open Access Journal |
| issn | 0144-5987 2048-4054 |
| language | English |
| last_indexed | 2024-04-13T11:49:21Z |
| publishDate | 2023-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Energy Exploration & Exploitation |
| spelling | doaj.art-4542cc601a164d65b55f96ce3653c6a92022-12-22T02:48:06ZengSAGE PublishingEnergy Exploration & Exploitation0144-59872048-40542023-01-014110.1177/01445987221112250A multi-energy load prediction of a building using the multi-layer perceptron neural network method with different optimization algorithmsZhongzhen Yan0Xinyuan Zhu1Xianglong Wang2Zhiwei Ye3Feng Guo4Lei Xie5Guiju Zhang6 School of Computer Science and Technology, , Wuhan, China School of Computer Science and Technology, , Wuhan, China School of Computer Science and Technology, , Wuhan, China School of Computer Science and Technology, , Wuhan, China China Railway Seventh Bureau Group Electrical Engineering Co. Ltd, Zhengzhou, China National Engineering Research Centre for Water Transport Safety (WTSC), Wuhan, China Key Laboratory of Hunan Province for Efficient Power System and Intelligent Manufacturing, Hunan, ChinaSince cooling and heating loads are recognized as key characteristics for evaluating the energy efficiency of buildings, it appears indisputable that they must be predicted and analyzed for residential structures. Accordingly, the multi-layer perceptron neural network is applied for predicting the heating and cooling loads using the experimental dataset. The used dataset is obtained by monitoring the impact of the building's dimensions on energy consumption. To optimize the training process of the multi-layer perceptron neural network, several optimizers are employed. Besides, different statistical performance indicators are considered to see which selected optimizer outperforms the rest in terms of accuracy and authenticity. The obtained results emphasize the remarkable performance of adaptive chaotic grey wolf optimization, which can be used to train the multi-layer perceptron neural network and forecast the buildings’ energy consumption with the highest accuracy. According to the obtained results, the hybrid multi-layer perceptron neural network- adaptive chaotic grey wolf optimization method demonstrates the best performance. The optimum number of neurons in the hidden layer is obtained to be 15. Also, based on the statistical performance indicators, the selected method reveals an R 2 of 0.9123 and 0.9419 for cooling and heating loads, respectively.https://doi.org/10.1177/01445987221112250 |
| spellingShingle | Zhongzhen Yan Xinyuan Zhu Xianglong Wang Zhiwei Ye Feng Guo Lei Xie Guiju Zhang A multi-energy load prediction of a building using the multi-layer perceptron neural network method with different optimization algorithms Energy Exploration & Exploitation |
| title | A multi-energy load prediction of a building using the multi-layer perceptron neural network method with different optimization algorithms |
| title_full | A multi-energy load prediction of a building using the multi-layer perceptron neural network method with different optimization algorithms |
| title_fullStr | A multi-energy load prediction of a building using the multi-layer perceptron neural network method with different optimization algorithms |
| title_full_unstemmed | A multi-energy load prediction of a building using the multi-layer perceptron neural network method with different optimization algorithms |
| title_short | A multi-energy load prediction of a building using the multi-layer perceptron neural network method with different optimization algorithms |
| title_sort | multi energy load prediction of a building using the multi layer perceptron neural network method with different optimization algorithms |
| url | https://doi.org/10.1177/01445987221112250 |
| work_keys_str_mv | AT zhongzhenyan amultienergyloadpredictionofabuildingusingthemultilayerperceptronneuralnetworkmethodwithdifferentoptimizationalgorithms AT xinyuanzhu amultienergyloadpredictionofabuildingusingthemultilayerperceptronneuralnetworkmethodwithdifferentoptimizationalgorithms AT xianglongwang amultienergyloadpredictionofabuildingusingthemultilayerperceptronneuralnetworkmethodwithdifferentoptimizationalgorithms AT zhiweiye amultienergyloadpredictionofabuildingusingthemultilayerperceptronneuralnetworkmethodwithdifferentoptimizationalgorithms AT fengguo amultienergyloadpredictionofabuildingusingthemultilayerperceptronneuralnetworkmethodwithdifferentoptimizationalgorithms AT leixie amultienergyloadpredictionofabuildingusingthemultilayerperceptronneuralnetworkmethodwithdifferentoptimizationalgorithms AT guijuzhang amultienergyloadpredictionofabuildingusingthemultilayerperceptronneuralnetworkmethodwithdifferentoptimizationalgorithms AT zhongzhenyan multienergyloadpredictionofabuildingusingthemultilayerperceptronneuralnetworkmethodwithdifferentoptimizationalgorithms AT xinyuanzhu multienergyloadpredictionofabuildingusingthemultilayerperceptronneuralnetworkmethodwithdifferentoptimizationalgorithms AT xianglongwang multienergyloadpredictionofabuildingusingthemultilayerperceptronneuralnetworkmethodwithdifferentoptimizationalgorithms AT zhiweiye multienergyloadpredictionofabuildingusingthemultilayerperceptronneuralnetworkmethodwithdifferentoptimizationalgorithms AT fengguo multienergyloadpredictionofabuildingusingthemultilayerperceptronneuralnetworkmethodwithdifferentoptimizationalgorithms AT leixie multienergyloadpredictionofabuildingusingthemultilayerperceptronneuralnetworkmethodwithdifferentoptimizationalgorithms AT guijuzhang multienergyloadpredictionofabuildingusingthemultilayerperceptronneuralnetworkmethodwithdifferentoptimizationalgorithms |